提出一种基于图像残差的超分辨率重建算法。以原高分辨率图像与插值放大后图像之间的图像残差与低分辨率图像样本特征作为样本对,对其进行K均值分类,并对每类样本对采用KSVD(K-singular value decomposition)方法进行训练获得高、低分辨率字典对,然后根据测试样本与类中心的欧氏距离选择字典对,以与测试样本相近的多个类别所重建的结果加权获得图像残差,并结合低分辨率图像的插值结果获得高分辨率图像。实验结果表明,提出的方法具有更高的重建质量,且采用训练样本分类和相近类别的重建结果的加权和有利于提高图像重建质量。

An image super-resolution (SR) reconstruction algorithm based on residual error is proposed. Patch pairs, composed of features for low-resolution (LR) patches and residual errors between original high-resolution (HR) image patches and interpolated LR image patches, are classified by K-means, Each class patch pair is trained by KSVD (K-singular value decomposition) to obtain an LR and HR dictionary pair. Residual errors are reconstructed by the dictionary pairs selected by the Euclidean distance between the test patches and class centers and by the weighted sum of the reconstructed results of the similar class patches. Then, combined with interpolated LR images and reconstructed residual errors, HR images are reconstructed. Experimental results show that the proposed method has a better performance and the method to classify patches and perform weight sum of the reconstructed results of the similar class patches is improving the quality of the SR image.